I am going to explain models predicting players market value using permutation variable importance. I use fifa-23 dataset. Models that I use are Random Forest Regressor, CatBoost Regressor and MLP Regressor.
First model that I use is RandomForestRegressor. I have already used and shortly described in homework 1.
Coefficient of determination: 0.77 Scatter plot: predictions vs truth
MLP - multi-layer perceptron is a neural network that consists of at least 3 layers. One layer usually consist of a linear and an activation.
Coefficient of determination: 0.81 Scatter plot: predictions vs truth
Third model is Catboost Regressor. The main idea of boosting is to sequentially combine many weak models and thus through greedy search create a strong competitive predictive model.
Coefficient of determination: 0.82 Scatter plot: predictions vs truth
Here I used Random Forest again but with normalized data. As expected there is no much difference between previous usage.
Coefficient of determination: 0.77 Scatter plot: predictions vs truth